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Databricks Databricks-Certified-Data-Engineer-Associate Exam Syllabus Topics:

Section	Objectives
Databricks Lakehouse Platform Fundamentals	- Clusters, notebooks, and basic Databricks environment usage - Workspace, architecture, and core platform concepts
Productionizing Data Pipelines	- Databricks Workflows / Jobs orchestration - Pipeline deployment and operationalization - Scheduling and monitoring jobs
Data Processing and Transformations	- Delta Lake fundamentals (tables, transactions, optimization) - User-defined functions (UDFs) - Apache Spark SQL operations (joins, aggregations, filtering) - PySpark DataFrame transformations
Data Ingestion and ELT Development	- Handling structured and semi-structured data - ETL patterns and transformations - Data ingestion using Spark SQL and PySpark
Data Governance and Quality	- Data quality concepts and management - Unity Catalog basics - Data access control and governance

>> Databricks-Certified-Data-Engineer-Associate試験勉強書 <<

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Databricks Certified Data Engineer Associate Exam 認定 Databricks-Certified-Data-Engineer-Associate 試験問題 (Q50-Q55):

質問 # 50
A data engineer is troubleshooting two different pipeline failures:
Pipeline A fails with a java.lang.OutOfMemoryError immediately after display(df.collect()) is called on a 100 GB dataset.
Pipeline B fails during a wide transformation joining two large tables, with an ExecutorLostFailure message indicating executor-memory exhaustion during the shuffle.
Which action should the data engineer take to address these two issues?

A. For Pipeline A, address driver memory by removing the full collect() or increasing driver capacity; for Pipeline B, increase shuffle partitions or executor memory.
B. For Pipeline A, switch to a storage-optimized cluster; for Pipeline B, use a compute-optimized cluster.
C. For Pipeline A, increase the number of executors; for Pipeline B, increase driver memory.
D. For both pipelines, enable Adaptive Query Execution because it automatically manages all driver and executor memory allocation.

正解：A

質問 # 51
A data analyst has a series of queries in a SQL program. The data analyst wants this program to run every day. They only want the final query in the program to run on Sundays. They ask for help from the data engineering team to complete this task.
Which of the following approaches could be used by the data engineering team to complete this task?

A. They could redesign the data model to separate the data used in the final query into a new table.
B. They could only run the entire program on Sundays.
C. They could wrap the queries using PySpark and use Python's control flow system to determine when to run the final query.
D. They could automatically restrict access to the source table in the final query so that it is only accessible on Sundays.
E. They could submit a feature request with Databricks to add this functionality.

正解：C

質問 # 52
Which of the following Structured Streaming queries is performing a hop from a Silver table to a Gold table?

正解：D

解説：
The best practice is to use "Complete" as output mode instead of "append" when working with aggregated tables. Since gold layer is work final aggregated tables, the only option with output mode as complete is option

質問 # 53
Which Databricks Asset Bundle format is valid?

A. configuration = {
" resources " : {
" jobs " : {
" name " : " hello-job " ,
" tasks " : {
" task_key " : " hello-task " ,
" existing_cluster_id " : " 1234-567890-abcde123 " ,
" notebook_task " : {
" notebook_path " : " ./hello.py "
}
}
}
}
}
B. resources:
jobs:
hello-job:
name: hello-job
tasks:
- task_key: hello-task
existing_cluster_id: 1234-567890-abcde123
notebook_task:
notebook_path: ./hello.py
C. resources {
jobs {
name = " hello-job "
tasks {
task_key = " hello-task "
existing_cluster_id = " 1234-567890-abcde123 "
notebook_task {
notebook_path = " ./hello.py "
}
}
}
}
D. {
" resources " : {
" jobs " : {
" name " : " hello-job " ,
" tasks " : {
" task_key " : " hello-task " ,
" existing_cluster_id " : " 1234-567890-abcde123 " ,
" notebook_task " : {
" notebook_path " : " ./hello.py "
}
}
}
}
}

正解：B

解説：
Databricks Asset Bundles use YAML configuration, and Databricks documentation specifies that bundle configuration is expressed in a root databricks.yml file. The resources mapping is used to define supported objects such as jobs , and job definitions include keys like name and tasks. Tasks in a job are defined under the tasks key, and a notebook task is represented with notebook_task and notebook_path. Option A is the only answer written in the correct general YAML bundle structure and matches the documented style of defining bundle resources. Options B and C use JSON- and Python-style syntax rather than bundle YAML. Option D uses Terraform/HCL-style syntax, which is not the Databricks Asset Bundle format. While a full production bundle often includes more top-level settings such as bundle, targets, or workspace, the question asks which format is valid, and only option A matches the documented Databricks Asset Bundles configuration model.
=========

質問 # 54
A data engineer needs to apply custom logic to string column city in table stores for a specific use case. In order to apply this custom logic at scale, the data engineer wants to create a SQL user-defined function (UDF).
Which of the following code blocks creates this SQL UDF?

正解：C

解説：
Explanation
https://www.databricks.com/blog/2021/10/20/introducing-sql-user-defined-functions.html

質問 # 55
......

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